How do you analyze and improve queries with query optimization and execution plans?

1 What is query optimization?

Query optimization is the process of finding the most efficient way to execute a query, given the available resources, data, and constraints. Query optimization involves analyzing the query, the data, the schema, the indexes, the statistics, and the configuration of the database system, and generating alternative plans for executing the query. The database engine then compares the estimated costs and benefits of each plan, and chooses the one that minimizes the resource consumption and maximizes the query performance.

2 What is an execution plan?

An execution plan is a graphical or textual representation of how the database engine executes a query. It shows the steps, operations, and algorithms that the database engine uses to retrieve, join, sort, filter, aggregate, and output the data. It also shows the estimated costs, cardinalities, and row sizes of each step, as well as the indexes, partitions, and statistics that are used or updated by the query. An execution plan helps you understand how the database engine interprets and optimizes your query, and where the potential performance issues are.

3 How do you generate an execution plan?

There are different ways to generate an execution plan, depending on the database system and the tool you are using. For example, in SQL Server Management Studio, you can use the Query menu or the toolbar to display the estimated or actual execution plan of a query. In Oracle SQL Developer, you can use the Explain Plan or Autotrace features to generate an execution plan. In MySQL Workbench, you can use the Explain or Explain Analyze commands to generate an execution plan. You can also use third-party tools or applications that support generating and analyzing execution plans.

4 How do you analyze an execution plan?

Analyzing an execution plan requires some knowledge and experience with the database system and the query language, however, there are some general guidelines and tips that can help you identify potential performance issues. For example, look for expensive or complex operations, such as table scans, nested loops, hash joins, sorts, or aggregations, to see if they can be avoided or reduced by using indexes, partitions, filters, or other techniques. Additionally, investigate discrepancies between the estimated and actual costs, cardinalities, and row sizes to determine if they are caused by outdated or inaccurate statistics, skewed data distribution, or parameter sniffing. Warnings and alerts should also be addressed; such as missing indexes, implicit conversions, or parallelism issues; by adding or modifying indexes, changing data types, or adjusting the configuration settings. Finally, consider opportunities to improve the query design by simplifying the logic and eliminating unnecessary columns or calculations; as well as using subqueries or common table expressions and set-based operations instead of cursors or loops.

5 How do you improve your queries with query optimization and execution plans?

Improving your queries with query optimization and execution plans is an iterative and continuous process that involves testing, measuring, and comparing different query plans and options. You can use various tools and methods to monitor and evaluate the performance of your queries, such as query store, performance monitor, dynamic management views, or trace and profiler. You can also use various techniques and best practices to optimize your query design and execution, such as indexing, partitioning, caching, batching, or tuning. However, you should always consider the trade-offs and impacts of each optimization on the overall system performance, resource consumption, and data integrity.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?